A number of devices for facial recognition are present in the market today, such as the facial recognition systems made by Visionics, Viisage, and Miros. Most of these systems make use of one or both of two main facial feature detection algorithms, eigenface and local feature analysis, and generally work by first recognizing a face in general and then performing feature measurements to find corresponding matches in a data base. To recognize the face in general, multi-scale algorithms are used to search a field of view at a low resolution in order to detect a general facial shape. Once the facial shape is detected, alignment begins in which the head position, size, and pose are determined. An image is then normalized and facial data is translated into unique code, which allows for easier comparison to stored data.
One limitation of the above systems is that the face must be angled at a certain angle, for example, 35 degrees and above, toward the camera for the image to be taken. Furthermore, since most of these systems only examine geometrical shapes, sizes, and locations of facial features, they cannot easily tell the difference between a real person's face and a rubber mode or photograph of the person's face, and can thus be easily fooled by someone attempting to bypass a security system utilizing one of the facial recognition systems. Therefore, there is a need for a more precise facial recognition method and system that are not susceptible to the above types of fraud.